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funding

Startup Founders Are Using AI To File Patents. Here’s Where DIY Becomes A Liability.

AI can help startups reduce patent preparation costs, but DIY applications may overlook critical issues. In this guest commentary, attorneys Michael Drapkin and Nathan Mutter of AI-Native law firm Landfall IP, argue that combining AI with legal expertise can produce more efficient, commercially valuable patent protection that withstands investor scrutiny and is harder for competitors to circumvent.

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Crunchbase News
coding-engineering

Vercel Sandbox 快照创建速度现已提升至原来的 10 倍

Vercel Sandbox 的快照创建速度提升了高达 10 倍，中位时间从 4.4 秒降至不足 0.4 秒，恢复速度也提升了 50%，p50 下降至 250 毫秒。这些优化利用了 Vercel Hive Snapshot (VHS) 格式及其更高效的数据索引，支持并行读写操作，并能更快地恢复基础镜像和用户数据。持久化沙盒现在会在会话结束时自动创建快照，并默认从最新快照恢复；所有新快照均采用新格式，而现有快照则保留旧格式。

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Vercel
copyright-privacy

Adaptive Depth Aggregation in Graph Transformers for Long-Range Graph Reasoning

arXiv:2611.08500v1 Announce Type: new Abstract: Graph Transformers improve long-range node-to-node communication through global or structure-aware interactions. However, improving cross-node communication does not determine how computations produced at different depths should be combined. Standard residual streams accumulate successive computations with fixed unit coefficients shared across nodes. This fixed aggregation is a residual-interface constraint: once intermediate contributions have been produced, the interface cannot explicitly reweight them by node. We hypothesize that this constraint can create a depth-wise aggregation mismatch when the relative utility of these intermediate contributions varies across nodes and structural contexts. To study this hypothesis, we formalize Graph Transformer residual computation as a causal node-wise history of graph-propagation and feed-forward network (FFN) contributions, making depth aggregation an explicit interface choice. We realize node-adaptive aggregation over this history with Attention Residuals (AttnRes) while preserving the backbone's graph-propagation and auxiliary-state machinery. Block AttnRes reduces the routing cost of long histories relative to Full AttnRes by pooling consecutive contributions. We instantiate the interface in GPS and GRIT and evaluate it on all five ECHO tasks. Both variants achieve lower mean absolute error (MAE) than their independently tuned backbones on every task, and at least one obtains the lowest mean among all compared methods in each task. Routing-only donor-swap interventions support the mismatch hypothesis, while additional ablations examine the key design choices. Together, these results support node-adaptive depth aggregation as a useful complement to cross-node communication in long-range graph reasoning.

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arXiv cs.LG
funding

Exclusive: FormAssembly overhauls its platform to expand into AI-powered process automation

FormAssembly Inc., a provider of online data collection and workflow automation software, today announced a complete platform overhaul that expands its capabilities beyond building forms to automating complex business processes using artificial intelligence. The new platform, which the company said took seven months to develop, combines form creation, workflow automation, integrations and compliance management in […] The post Exclusive: FormAssembly overhauls its platform to expand into AI-powered process automation appeared first on SiliconANGLE.

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SiliconANGLE AI